





import logging
import os
import sys
from dataclasses import dataclass, field
from typing import Optional

import torch
from datasets import load_dataset
from torchvision.transforms import Compose, Lambda, Normalize, RandomHorizontalFlip, RandomResizedCrop, ToTensor
from torchvision.transforms.functional import InterpolationMode

import transformers
from transformers import (
    HfArgumentParser,
    Trainer,
    TrainingArguments,
    ViTImageProcessor,
    ViTMAEConfig,
    ViTMAEForPreTraining,
)
from transformers.trainer_utils import get_last_checkpoint
from transformers.utils import check_min_version, send_example_telemetry
from transformers.utils.versions import require_version
_a     :    List[str]         =						logging.getLogger(__name__)

# Will error if the minimal version of Transformers is not installed. Remove at your own risks.
check_min_version('4.31.0')

require_version('datasets>=1.8.0', 'To fix: pip install -r examples/pytorch/image-pretraining/requirements.txt')
@dataclass
class   a_ :
			A__      :			Optional[str]       	=  field(
			    default='cifar10'		,							metadata={'help': 'Name of a dataset from the datasets package'}      )
			A__      :			Optional[str]       	=  field(
			    default=a		,							metadata={'help': 'The configuration name of the dataset to use (via the datasets library).'}      )
			A__      :			Optional[str]       	=  field(
			    default=a		,							metadata={'help': 'The column name of the images in the files.'}      )
			A__      :			Optional[str]       	=  field(default=a		,							metadata={'help': 'A folder containing the training data.'}      )
			A__      :			Optional[str]       	=  field(default=a		,							metadata={'help': 'A folder containing the validation data.'}      )
			A__      :			Optional[float]       	=  field(
			    default=0.15		,							metadata={'help': 'Percent to split off of train for validation.'}      )
			A__      :			Optional[int]       	=  field(
			    default=a		,							metadata={
			        'help': (
			            'For debugging purposes or quicker training, truncate the number of training examples to this '
			            'value if set.'
			        )
			    }		,							)
			A__      :			Optional[int]       	=  field(
			    default=a		,							metadata={
			        'help': (
			            'For debugging purposes or quicker training, truncate the number of evaluation examples to this '
			            'value if set.'
			        )
			    }		,							)

			def      lowerCAmelCase(   self   :  str						):






							"""simple docstring"""
							snake_case   :					Any    						=				{}
							if self.train_dir is not None:
											snake_case   :					Dict    						=				self.train_dir
							if self.validation_dir is not None:
											snake_case   :					Optional[Any]    						=				self.validation_dir
							snake_case   :					int    						=				data_files if data_files else None
@dataclass
class   a_ :
			A__      :			str       	=  field(
			    default=a		,							metadata={
			        'help': (
			            'The model checkpoint for weights initialization.Don\'t set if you want to train a model from scratch.'
			        )
			    }		,							)
			A__      :			Optional[str]       	=  field(
			    default=a		,							metadata={'help': 'Pretrained config name or path if not the same as model_name_or_path'}      )
			A__      :			Optional[str]       	=  field(
			    default=a		,							metadata={
			        'help': (
			            'Override some existing default config settings when a model is trained from scratch. Example: '
			            'n_embd=10,resid_pdrop=0.2,scale_attn_weights=false,summary_type=cls_index'
			        )
			    }		,							)
			A__      :			Optional[str]       	=  field(
			    default=a		,							metadata={'help': 'Where do you want to store the pretrained models downloaded from s3'}      )
			A__      :			str       	=  field(
			    default='main'		,							metadata={'help': 'The specific model version to use (can be a branch name, tag name or commit id).'}		,							)
			A__      :			str       	=  field(default=a		,							metadata={'help': 'Name or path of preprocessor config.'}      )
			A__      :			bool       	=  field(
			    default=a		,							metadata={
			        'help': (
			            'Will use the token generated when running `huggingface-cli login` (necessary to use this script '
			            'with private models).'
			        )
			    }		,							)
			A__      :			float       	=  field(
			    default=0.75		,							metadata={'help': 'The ratio of the number of masked tokens in the input sequence.'}      )
			A__      :			bool       	=  field(
			    default=a		,							metadata={'help': 'Whether or not to train with normalized pixel values as target.'}      )
@dataclass
class   a_ (			a      ):
			A__      :			float       	=  field(
			    default=1e-3		,							metadata={'help': 'Base learning rate: absolute_lr = base_lr * total_batch_size / 256.'}      )
def 							a_	(    __magic_name__   )				->			Optional[int]:




				"""simple docstring"""






				snake_case   :					Optional[Any]    						=				torch.stack([example['''pixel_values'''] for example in examples]   )
				return {"pixel_values": pixel_values}
def 							a_	(    )				->			Any:




				"""simple docstring"""






				snake_case   :					Union[str, Any]    						=				HfArgumentParser((ModelArguments, DataTrainingArguments, CustomTrainingArguments)   )
				if len(sys.argv   ) == 2 and sys.argv[1].endswith('''.json'''   ):
								# If we pass only one argument to the script and it's the path to a json file,
								# let's parse it to get our arguments.
								snake_case						,						snake_case						,						snake_case   :					List[Any]    						=				parser.parse_json_file(json_file=os.path.abspath(sys.argv[1]   )   )
				else:
								snake_case						,						snake_case						,						snake_case   :					Tuple    						=				parser.parse_args_into_dataclasses()

				# Sending telemetry. Tracking the example usage helps us better allocate resources to maintain them. The
				# information sent is the one passed as arguments along with your Python/PyTorch versions.
				send_example_telemetry('''run_mae'''		,     __magic_name__		,     __magic_name__   )

				# Setup logging
				logging.basicConfig(
				    format='''%(asctime)s - %(levelname)s - %(name)s - %(message)s'''		,     datefmt='''%m/%d/%Y %H:%M:%S'''		,     handlers=[logging.StreamHandler(sys.stdout   )]		,     )

				if training_args.should_log:
								# The default of training_args.log_level is passive, so we set log level at info here to have that default.
								transformers.utils.logging.set_verbosity_info()

				snake_case   :					Optional[Any]    						=				training_args.get_process_log_level()
				logger.setLevel(__magic_name__   )
				transformers.utils.logging.set_verbosity(__magic_name__   )
				transformers.utils.logging.enable_default_handler()
				transformers.utils.logging.enable_explicit_format()

				# Log on each process the small summary:
				logger.warning(
				    F"Process rank: {training_args.local_rank}, device: {training_args.device}, n_gpu: {training_args.n_gpu}"
				    + F"distributed training: {bool(training_args.local_rank != -1   )}, 16-bits training: {training_args.fpaa}"   )
				logger.info(F"Training/evaluation parameters {training_args}"   )

				# Detecting last checkpoint.
				snake_case   :					Union[str, Any]    						=				None
				if os.path.isdir(training_args.output_dir   ) and training_args.do_train and not training_args.overwrite_output_dir:
								snake_case   :					Dict    						=				get_last_checkpoint(training_args.output_dir   )
								if last_checkpoint is None and len(os.listdir(training_args.output_dir   )   ) > 0:
												raise ValueError(
												    F"Output directory ({training_args.output_dir}) already exists and is not empty. "
												    '''Use --overwrite_output_dir to overcome.'''   )
								elif last_checkpoint is not None and training_args.resume_from_checkpoint is None:
												logger.info(
												    F"Checkpoint detected, resuming training at {last_checkpoint}. To avoid this behavior, change "
												    '''the `--output_dir` or add `--overwrite_output_dir` to train from scratch.'''   )

    # Initialize our dataset.
				snake_case   :					str    						=				load_dataset(
				    data_args.dataset_name		,     data_args.dataset_config_name		,     data_files=data_args.data_files		,     cache_dir=model_args.cache_dir		,     use_auth_token=True if model_args.use_auth_token else None		,     )

				# If we don't have a validation split, split off a percentage of train as validation.
				snake_case   :					Optional[Any]    						=				None if '''validation''' in ds.keys() else data_args.train_val_split
				if isinstance(data_args.train_val_split		,     __magic_name__   ) and data_args.train_val_split > 0.0:
								snake_case   :					Union[str, Any]    						=				ds['''train'''].train_test_split(data_args.train_val_split   )
								snake_case   :					List[Any]    						=				split['''train''']
								snake_case   :					Dict    						=				split['''test''']

				# Load pretrained model and image processor
				#
				# Distributed training:
				# The .from_pretrained methods guarantee that only one local process can concurrently
				# download model & vocab.
				snake_case   :					Optional[int]    						=				{
				    '''cache_dir''': model_args.cache_dir,
				    '''revision''': model_args.model_revision,
				    '''use_auth_token''': True if model_args.use_auth_token else None,
				}
				if model_args.config_name:
								snake_case   :					Any    						=				ViTMAEConfig.from_pretrained(model_args.config_name		,     **__magic_name__   )
				elif model_args.model_name_or_path:
								snake_case   :					Union[str, Any]    						=				ViTMAEConfig.from_pretrained(model_args.model_name_or_path		,     **__magic_name__   )
				else:
								snake_case   :					Tuple    						=				ViTMAEConfig()
								logger.warning('''You are instantiating a new config instance from scratch.'''   )
								if model_args.config_overrides is not None:
												logger.info(F"Overriding config: {model_args.config_overrides}"   )
												config.update_from_string(model_args.config_overrides   )
												logger.info(F"New config: {config}"   )

    # adapt config
				config.update(
				    {
				        '''mask_ratio''': model_args.mask_ratio,
				        '''norm_pix_loss''': model_args.norm_pix_loss,
				    }   )

				# create image processor
				if model_args.image_processor_name:
								snake_case   :					Optional[int]    						=				ViTImageProcessor.from_pretrained(model_args.image_processor_name		,     **__magic_name__   )
				elif model_args.model_name_or_path:
								snake_case   :					List[Any]    						=				ViTImageProcessor.from_pretrained(model_args.model_name_or_path		,     **__magic_name__   )
				else:
								snake_case   :					Tuple    						=				ViTImageProcessor()

				# create model
				if model_args.model_name_or_path:
								snake_case   :					List[str]    						=				ViTMAEForPreTraining.from_pretrained(
								    model_args.model_name_or_path		,     from_tf=bool('''.ckpt''' in model_args.model_name_or_path   )		,     config=__magic_name__		,     cache_dir=model_args.cache_dir		,     revision=model_args.model_revision		,     use_auth_token=True if model_args.use_auth_token else None		,     )
				else:
								logger.info('''Training new model from scratch'''   )
								snake_case   :					Optional[Any]    						=				ViTMAEForPreTraining(__magic_name__   )

				if training_args.do_train:
								snake_case   :					int    						=				ds['''train'''].column_names
				else:
								snake_case   :					Union[str, Any]    						=				ds['''validation'''].column_names

				if data_args.image_column_name is not None:
								snake_case   :					Optional[int]    						=				data_args.image_column_name
				elif "image" in column_names:
								snake_case   :					Union[str, Any]    						=				'''image'''
				elif "img" in column_names:
								snake_case   :					List[Any]    						=				'''img'''
				else:
								snake_case   :					int    						=				column_names[0]

				# transformations as done in original MAE paper
				# source: https://github.com/facebookresearch/mae/blob/main/main_pretrain.py
				if "shortest_edge" in image_processor.size:
								snake_case   :					Any    						=				image_processor.size['''shortest_edge''']
				else:
								snake_case   :					Union[str, Any]    						=				(image_processor.size['''height'''], image_processor.size['''width'''])
				snake_case   :					Any    						=				Compose(
				    [
				        Lambda(lambda __magic_name__   : img.convert('''RGB'''   ) if img.mode != "RGB" else img   ),
				        RandomResizedCrop(__magic_name__		,     scale=(0.2, 1.0)		,     interpolation=InterpolationMode.BICUBIC   ),
				        RandomHorizontalFlip(),
				        ToTensor(),
				        Normalize(mean=image_processor.image_mean		,     std=image_processor.image_std   ),
				    ]   )

				def preprocess_images(__magic_name__   ):
								snake_case   :					Dict    						=				[transforms(__magic_name__   ) for image in examples[image_column_name]]
								return examples

				if training_args.do_train:
								if "train" not in ds:
												raise ValueError('''--do_train requires a train dataset'''   )
								if data_args.max_train_samples is not None:
												snake_case   :					int    						=				ds['''train'''].shuffle(seed=training_args.seed   ).select(range(data_args.max_train_samples   )   )
								# Set the training transforms
								ds["train"].set_transform(__magic_name__   )

				if training_args.do_eval:
								if "validation" not in ds:
												raise ValueError('''--do_eval requires a validation dataset'''   )
								if data_args.max_eval_samples is not None:
												snake_case   :					int    						=				(
												    ds['''validation'''].shuffle(seed=training_args.seed   ).select(range(data_args.max_eval_samples   )   )
												)
								# Set the validation transforms
								ds["validation"].set_transform(__magic_name__   )

				# Compute absolute learning rate
				snake_case   :					Union[str, Any]    						=				(
				    training_args.train_batch_size * training_args.gradient_accumulation_steps * training_args.world_size
				)
				if training_args.base_learning_rate is not None:
								snake_case   :					Optional[Any]    						=				training_args.base_learning_rate * total_train_batch_size / 256

				# Initialize our trainer
				snake_case   :					Any    						=				Trainer(
				    model=__magic_name__		,     args=__magic_name__		,     train_dataset=ds['''train'''] if training_args.do_train else None		,     eval_dataset=ds['''validation'''] if training_args.do_eval else None		,     tokenizer=__magic_name__		,     data_collator=__magic_name__		,     )

				# Training
				if training_args.do_train:
								snake_case   :					str    						=				None
								if training_args.resume_from_checkpoint is not None:
												snake_case   :					Optional[Any]    						=				training_args.resume_from_checkpoint
								elif last_checkpoint is not None:
												snake_case   :					List[str]    						=				last_checkpoint
								snake_case   :					Union[str, Any]    						=				trainer.train(resume_from_checkpoint=__magic_name__   )
								trainer.save_model()
								trainer.log_metrics('''train'''		,     train_result.metrics   )
								trainer.save_metrics('''train'''		,     train_result.metrics   )
								trainer.save_state()

				# Evaluation
				if training_args.do_eval:
								snake_case   :					Union[str, Any]    						=				trainer.evaluate()
								trainer.log_metrics('''eval'''		,     __magic_name__   )
								trainer.save_metrics('''eval'''		,     __magic_name__   )

				# Write model card and (optionally) push to hub
				snake_case   :					Tuple    						=				{
				    '''tasks''': '''masked-auto-encoding''',
				    '''dataset''': data_args.dataset_name,
				    '''tags''': ['''masked-auto-encoding'''],
				}
				if training_args.push_to_hub:
								trainer.push_to_hub(**__magic_name__   )
				else:
								trainer.create_model_card(**__magic_name__   )
def 							a_	(    __magic_name__   )				->			Union[str, Any]:




				"""simple docstring"""






				main()


if __name__ == "__main__":
			main()






'''simple docstring'''







from argparse import ArgumentParser

from ..pipelines import Pipeline, PipelineDataFormat, get_supported_tasks, pipeline
from ..utils import logging
from . import BaseTransformersCLICommand


lowerCAmelCase_     						=				logging.get_logger(__name__)  # pylint: disable=invalid-name







def 						_A      ( UpperCAmelCase						):






   '''simple docstring'''







   if not path:
      return "pipe"

   for ext in PipelineDataFormat.SUPPORTED_FORMATS:
      if path.endswith(UpperCAmelCase						):
         return ext

   raise Exception(
       F"""Unable to determine file format from file extension {path}. """
       F"""Please provide the format through --format {PipelineDataFormat.SUPPORTED_FORMATS}"""						)







def 						_A      ( UpperCAmelCase						):






   '''simple docstring'''







   A__       =     pipeline(
       task=args.task      ,model=args.model if args.model else None      ,config=args.config      ,tokenizer=args.tokenizer      ,device=args.device      ,)
   A__       =     try_infer_format_from_ext(args.input						) if args.format == 'infer' else args.format
   A__       =     PipelineDataFormat.from_str(
       format=UpperCAmelCase      ,output_path=args.output      ,input_path=args.input      ,column=args.column if args.column else nlp.default_input_names      ,overwrite=args.overwrite      ,)
   return RunCommand(UpperCAmelCase      ,UpperCAmelCase						)







class 	_snake_case(			UpperCAmelCase      ):






 def __init__(self       :  Tuple    ,		a       :  Pipeline    ,		a       :  PipelineDataFormat    )       ->   Union[str, Any]:






    """simple docstring"""


    A__       =     nlp
    A__       =     reader






 @staticmethod
 def 						_UpperCamelCase (a       :  ArgumentParser    )       ->   str:






    """simple docstring"""


    A__       =     parser.add_parser('run'    ,		help='Run a pipeline through the CLI'    )
    run_parser.add_argument('--task'    ,		choices=get_supported_tasks()    ,		help='Task to run'    )
    run_parser.add_argument('--input'    ,		type=a    ,		help='Path to the file to use for inference'    )
    run_parser.add_argument('--output'    ,		type=a    ,		help='Path to the file that will be used post to write results.'    )
    run_parser.add_argument('--model'    ,		type=a    ,		help='Name or path to the model to instantiate.'    )
    run_parser.add_argument('--config'    ,		type=a    ,		help='Name or path to the model\'s config to instantiate.'    )
    run_parser.add_argument(
        '--tokenizer'    ,		type=a    ,		help='Name of the tokenizer to use. (default: same as the model name)'    )
    run_parser.add_argument(
        '--column'    ,		type=a    ,		help='Name of the column to use as input. (For multi columns input as QA use column1,columns2)'    ,		)
    run_parser.add_argument(
        '--format'    ,		type=a    ,		default='infer'    ,		choices=PipelineDataFormat.SUPPORTED_FORMATS    ,		help='Input format to read from'    ,		)
    run_parser.add_argument(
        '--device'    ,		type=a    ,		default=-1    ,		help='Indicate the device to run onto, -1 indicates CPU, >= 0 indicates GPU (default: -1)'    ,		)
    run_parser.add_argument('--overwrite'    ,		action='store_true'    ,		help='Allow overwriting the output file.'    )
    run_parser.set_defaults(func=a    )






 def 						_UpperCamelCase (self       :  int    )       ->   Optional[Any]:






    """simple docstring"""


    A__  ,			A__       =     self._nlp, []

    for entry in self._reader:
       A__       =     nlp(**a    ) if self._reader.is_multi_columns else nlp(a    )
       if isinstance(a    ,		a    ):
          outputs.append(a    )
       else:
          outputs += output

        # Saving data
    if self._nlp.binary_output:
       A__       =     self._reader.save_binary(a    )
       logger.warning(f"""Current pipeline requires output to be in binary format, saving at {binary_path}"""    )
    else:
       self._reader.save(a    )


